Combining Fourier-Based Channel Attention with TCN and Informer for Railway Accident Prediction

The rapid expansion of railway networks and technological advancements has introduced significant challenges in ensuring railway safety. To address the critical need for effective prediction and prevention mechanisms, this paper proposes the FCAM-TCN-DSInformer model, a novel approach to railway accident prediction. The model integrates the Fourier-based Channel Attention Mechanism (FCAM), Temporal Convolutional Network (TCN), and De-stationary Informer (DSInformer) to capture both temporal and spectral features from time series data effectively. Extensive experiments and ablation studies were conducted to validate the model's performance. The results demonstrate that the FCAM-TCN-DSInformer model significantly outperforms both traditional and advanced models, including ARIMA, LSTM, GRU, Transformer, Autoformer, and FEDformer by achieving the highest <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{{2}}$</tex> score and substantial reductions in error metrics such as RMSE, MAE, and MAPE. These improvements confirm the model's accuracy and robustness, making it a powerful tool for railway safety management. Moreover, the model's adaptability to different univariate time series datasets, such as the Daily Minimum Temperatures Dataset and the US Births Dataset, highlights its strong generalization capabilities. Future work will focus on optimizing the model for real-time predictions and expanding its application to multi-step prediction tasks, thereby fully realizing its potential across diverse forecasting contexts.